HP2PC: Scalable Hierarchically-Distributed Peer-to-Peer Clustering

In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a hierarchically-distributed peer-to-peer architecture and algorithm for data clustering (HP2PC). The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Peers at a certain level of the hierarchy cooperate within their respective neighborhoods to perform clustering. Using this model, we can partition the clustering problem in a modular way, solve each part individually, then successively combine clusterings up the hierarchy where increasingly global solutions are computed. The algorithm was applied to a distributed document clustering problem and achieved decent speedup with comparable clustering quality to the centralized approach.